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dc.contributor.authorRydow, Erik
dc.contributor.authorBorgo, Rita
dc.contributor.authorFang, Hui
dc.contributor.authorTorsney-weir, Thomas
dc.contributor.authorSwallow, Ben
dc.contributor.authorPorphyre, Thibaud
dc.contributor.authorTurkay, Cagatay
dc.contributor.authorChen, Min
dc.date.accessioned2022-10-26T10:30:15Z
dc.date.available2022-10-26T10:30:15Z
dc.date.issued2022-09-29
dc.identifier281565358
dc.identifier251b188d-0676-432a-b550-2765221a7386
dc.identifier85139505001
dc.identifier.citationRydow , E , Borgo , R , Fang , H , Torsney-weir , T , Swallow , B , Porphyre , T , Turkay , C & Chen , M 2022 , ' Development and evaluation of two approaches of visual sensitivity analysis to support epidemiological modeling ' , IEEE Transactions on Visualization and Computer Graphics , vol. Early Access , 9906007 . https://doi.org/10.1109/TVCG.2022.3209464en
dc.identifier.issn1077-2626
dc.identifier.othercrossref: 10.1109/TVCG.2022.3209464
dc.identifier.otherORCID: /0000-0002-0227-2160/work/120434281
dc.identifier.urihttps://hdl.handle.net/10023/26249
dc.descriptionFunding information: Authors would like to thank UKRI/EPSRC “RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19” (EP/V054236/1), Scottish Government Rural and Environment Science and Analytical Services Division, Centre of Expertise on Animal Disease Outbreaks (EPIC), French National Research Agency and Boehringer Ingelheim Animal Health France for support through the IDEXLYON project (ANR-16-IDEX-0005), the Industrial Chair in Veterinary Public Health, as part of Lyon VPH Hub.en
dc.description.abstractComputational modeling is a commonly used technology in many scientific disciplines and has played a noticeable role in combating the COVID-19 pandemic. Modeling scientists conduct sensitivity analysis frequently to observe and monitor the behavior of a model during its development and deployment. The traditional algorithmic ranking of sensitivity of different parameters usually does not provide modeling scientists with sufficient information to understand the interactions between different parameters and model outputs, while modeling scientists need to observe a large number of model runs in order to gain actionable information for parameter optimization. To address the above challenge, we developed and compared two visual analytics approaches, namely: algorithm-centric and visualization-assisted , and visualization-centric and algorithm-assisted. We evaluated the two approaches based on a structured analysis of different tasks in visual sensitivity analysis as well as the feedback of domain experts. While the work was carried out in the context of epidemiological modeling, the two approaches developed in this work are directly applicable to a variety of modeling processes featuring time series outputs, and can be extended to work with models with other types of outputs.
dc.format.extent11
dc.format.extent2316571
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Visualization and Computer Graphicsen
dc.subjectSensitivity analysisen
dc.subjectEnsemble visualizationen
dc.subjectCOVID-19en
dc.subjectEpidemiological modelingen
dc.subjectEpidemiologyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectNDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subjectNCADen
dc.subjectACen
dc.subject.lccQA75en
dc.subject.lccRA0421en
dc.titleDevelopment and evaluation of two approaches of visual sensitivity analysis to support epidemiological modelingen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doi10.1109/TVCG.2022.3209464
dc.description.statusPeer revieweden
dc.identifier.urlhttps://repository.lboro.ac.uk/articles/journal_contribution/Development_and_evaluation_of_two_approaches_of_visual_sensitivity_analysis_to_support_epidemiological_modeling/21257793en


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